Design and Validation of a Simulator for Equine Joint Injections
Bibliographic record
Abstract
Joint injections are commonly used in equine practice for diagnosis and treatment of joint disorders. Performing joint injections is hence an essential skill for equine practitioners. However, opportunities for veterinary students to practice this skill are often scarce in veterinary curricula. The aim of this study was to design and validate an equine joint injection simulator. We hypothesized that the simulator will enhance student ability and confidence in performing joint injections. The simulator was constructed around an equine forelimb skeleton with soft tissues rebuilt using building foam and rubber bands. An electrical circuit including a buzzer, a battery, wire wool in the joints, and a hypodermic needle at the end of the cable was incorporated. If the students placed the needle into the joint correctly, instant auditory feedback was provided by the buzzer. To validate the simulator, 45 veterinary students were allocated to three groups: cadaver limb, textbook, or simulator. Students' ability to perform joint injections was tested and students' opinions were evaluated with a questionnaire. The proportion of students performing a metacarpophalangeal (MCP) joint injection correctly was significantly higher in the cadaver (93%) and simulator (76%) groups compared to the textbook group (50%). There was no significant difference between groups for performing a distal interphalangeal (DIP) joint injection correctly. Students rated the learning experience with the cadaver and simulator group high and with the textbook group low. The joint injection simulator represents an affordable teaching aid that allows students to repeatedly practice this skill in their own time with immediate feedback.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".